Template-free 2D particle fusion in localization microscopy

ECLiPSE: a versatile classification technique for structural and morphological analysis of 2D and 3D single-molecule localization microscopy data

Single-molecule localization microscopy (SMLM) has gained widespread use for visualizing the morphology of subcellular organelles and structures with nanoscale spatial resolution. However, analysis tools for automatically quantifying and classifying SMLM images have lagged behind. Here we introduce Enhanced Classification of Localized Point clouds by Shape Extraction (ECLiPSE), an automated machine learning analysis pipeline specifically designed to classify cellular structures captured through two-dimensional or three-dimensional SMLM. ECLiPSE leverages a comprehensive set of shape descriptors, the majority of which are directly extracted from the localizations to minimize bias during the characterization of individual structures. ECLiPSE has been validated using both unsupervised and supervised classification on datasets, including various cellular structures, achieving near-perfect accuracy. We apply two-dimensional ECLiPSE to classify morphologically distinct protein aggregates relevant for neurodegenerative diseases. Additionally, we employ three-dimensional ECLiPSE to identify relevant biological differences between healthy and depolarized mitochondria. ECLiPSE will enhance the way we study cellular structures across various biological contexts.

TorsinA is essential for neuronal nuclear pore complex localization and maturation

As lifelong interphase cells, neurons face an array of unique challenges. A key challenge is regulating nuclear pore complex (NPC) biogenesis and localization, the mechanisms of which are largely unknown. Here we identify neuronal maturation as a period of strongly upregulated NPC biogenesis. We demonstrate that the AAA+ protein torsinA, whose dysfunction causes the neurodevelopmental movement disorder DYT-TOR1A dystonia and co-ordinates NPC spatial organization without impacting total NPC density. We generated an endogenous Nup107-HaloTag mouse line to directly visualize NPC organization in developing neurons and find that torsinA is essential for proper NPC localization. In the absence of torsinA, the inner nuclear membrane buds excessively at sites of mislocalized nascent NPCs, and the formation of complete NPCs is delayed. Our work demonstrates that NPC spatial organization and number are independently determined and identifies NPC biogenesis as a process vulnerable to neurodevelopmental disease insults.

Machine Learning for Single-Molecule Localization Microscopy: From Data Analysis to Quantification

Single-molecule localization microscopy (SMLM) is a versatile tool for realizing nanoscale imaging with visible light and providing unprecedented opportunities to observe bioprocesses. The integration of machine learning with SMLM enhances data analysis by improving efficiency and accuracy. This tutorial aims to provide a comprehensive overview of the data analysis process and theoretical aspects of SMLM, while also highlighting the typical applications of machine learning in this field. By leveraging advanced analytical techniques, SMLM is becoming a powerful quantitative analysis tool for biological research.

UBAP2L ensures homeostasis of nuclear pore complexes at the intact nuclear envelope

Assembly of macromolecular complexes at correct cellular sites is crucial for cell function. Nuclear pore complexes (NPCs) are large cylindrical assemblies with eightfold rotational symmetry, built through hierarchical binding of nucleoporins (Nups) forming distinct subcomplexes. Here, we uncover a role of ubiquitin-associated protein 2-like (UBAP2L) in the assembly and stability of properly organized and functional NPCs at the intact nuclear envelope (NE) in human cells. UBAP2L localizes to the nuclear pores and facilitates the formation of the Y-complex, an essential scaffold component of the NPC, and its localization to the NE. UBAP2L promotes the interaction of the Y-complex with POM121 and Nup153, the critical upstream factors in a well-defined sequential order of Nups assembly onto NE during interphase. Timely localization of the cytoplasmic Nup transport factor fragile X-related protein 1 (FXR1) to the NE and its interaction with the Y-complex are likewise dependent on UBAP2L. Thus, this NPC biogenesis mechanism integrates the cytoplasmic and the nuclear NPC assembly signals and ensures efficient nuclear transport, adaptation to nutrient stress, and cellular proliferative capacity, highlighting the importance of NPC homeostasis at the intact NE.

Insights into protein structure using cryogenic light microscopy

Fluorescence microscopy has witnessed many clever innovations in the last two decades, leading to new methods such as structured illumination and super-resolution microscopies. The attainable resolution in biological samples is, however, ultimately limited by residual motion within the sample or in the microscope setup. Thus, such experiments are typically performed on chemically fixed samples. Cryogenic light microscopy (Cryo-LM) has been investigated as an alternative, drawing on various preservation techniques developed for cryogenic electron microscopy (Cryo-EM). Moreover, this approach offers a powerful platform for correlative microscopy. Another key advantage of Cryo-LM is the strong reduction in photobleaching at low temperatures, facilitating the collection of orders of magnitude more photons from a single fluorophore. This results in much higher localization precision, leading to Angstrom resolution. In this review, we discuss the general development and progress of Cryo-LM with an emphasis on its application in harnessing structural information on proteins and protein complexes.

Detecting continuous structural heterogeneity in single-molecule localization microscopy data

Fusion of multiple chemically identical complexes, so-called particles, in localization microscopy, can improve the signal-to-noise ratio and overcome under-labeling. To this end, structural homogeneity of the data must be assumed. Biological heterogeneity, however, could be present in the data originating from distinct conformational variations or (continuous) variations in particle shapes. We present a prior-knowledge-free method for detecting continuous structural variations with localization microscopy. Detecting this heterogeneity leads to more faithful fusions and reconstructions of the localization microscopy data as their heterogeneity is taken into account. In experimental datasets, we show the continuous variation of the height of DNA origami tetrahedrons imaged with 3D PAINT and of the radius of Nuclear Pore Complexes imaged in 2D with STORM. In simulation, we study the impact on the heterogeneity detection pipeline of Degree Of Labeling and of structural variations in the form of two independent modes.

Photoactivatable Carbo- and Silicon-Rhodamines and Their Application in MINFLUX Nanoscopy

New photoactivatable fluorescent dyes (rhodamine, carbo- and silicon-rhodamines [SiR]) with emission ranging from green to far red have been prepared, and their photophysical properties studied. The photocleavable 2-nitrobenzyloxycarbonyl unit with an alpha-carboxyl group as a branching point and additional functionality was attached to a polycyclic and lipophilic fluorescent dye. The photoactivatable probes having the HaloTagTM amine (O2) ligand bound with a dye core were obtained and applied for live-cell staining in stable cell lines incorporating Vimentin (VIM) or Nuclear Pore Complex Protein NUP96 fused with the HaloTag. The probes were applied in 2D (VIM, NUP96) and 3D (VIM) MINFLUX nanoscopy, as well as in superresolution fluorescence microscopy with single fluorophore activation (VIM, live-cell labeling). Images of VIM and NUPs labeled with different dyes were acquired and their apparent dimensions and shapes assessed on a lower single-digit nanometer scale. Applicability and performance of the photoactivatable dye derivatives were evaluated in terms of photoactivation rate, labeling and detection efficiency, number of detected photons per molecule and other parameters related to MINFLUX nanoscopy.

Particle fusion of super-resolution data reveals the unit structure of Nup96 in Nuclear Pore Complex

Single molecule localization microscopy offers resolution nearly down to the molecular level with specific molecular labelling, and is thereby a promising tool for structural biology. In practice, however, the actual value to this field is limited primarily by incomplete fluorescent labelling of the structure. This missing information can be completed by merging information from many structurally identical particles in a particle fusion approach similar to cryo-EM single-particle analysis. In this paper, we present a data analysis of particle fusion results of fluorescently labelled Nup96 nucleoporins in the Nuclear Pore Complex to show that Nup96 occurs in a spatial arrangement of two rings of 8 units with two Nup96 copies per unit giving a total of 32 Nup96 copies per pore. We use Artificial Intelligence assisted modeling in Alphafold to extend the existing cryo-EM model of Nup96 to accurately pinpoint the positions of the fluorescent labels and show the accuracy of the match between fluorescent and cryo-EM data to be better than 3 nm in-plane and 5 nm out-of-plane.

Direct observation of the conformational states of PIEZO1

PIEZOs are mechanosensitive ion channels that convert force into chemoelectric signals1,2 and have essential roles in diverse physiological settings3. In vitro studies have proposed that PIEZO channels transduce mechanical force through the deformation of extensive blades of transmembrane domains emanating from a central ion-conducting pore4-8. However, little is known about how these channels interact with their native environment and which molecular movements underlie activation. Here we directly observe the conformational dynamics of the blades of individual PIEZO1 molecules in a cell using nanoscopic fluorescence imaging. Compared with previous structural models of PIEZO1, we show that the blades are significantly expanded at rest by the bending stress exerted by the plasma membrane. The degree of expansion varies dramatically along the length of the blade, where decreased binding strength between subdomains can explain increased flexibility of the distal blade. Using chemical and mechanical modulators of PIEZO1, we show that blade expansion and channel activation are correlated. Our findings begin to uncover how PIEZO1 is activated in a native environment. More generally, as we reliably detect conformational shifts of single nanometres from populations of channels, we expect that this approach will serve as a framework for the structural analysis of membrane proteins through nanoscopic imaging.

ECLiPSE: A Versatile Classification Technique for Structural and Morphological Analysis of Super-Resolution Microscopy Data

We introduce a new automated machine learning analysis pipeline to precisely classify cellular structures captured through single molecule localization microscopy, which we call ECLiPSE (Enhanced Classification of Localized Pointclouds by Shape Extraction). ECLiPSE leverages 67 comprehensive shape descriptors encompassing geometric, boundary, skeleton and other properties, the majority of which are directly extracted from the localizations to accurately characterize individual structures. We validate ECLiPSE through unsupervised and supervised classification on a dataset featuring five distinct cellular structures, achieving exceptionally high classification accuracies nearing 100%. Moreover, we demonstrate the versatility of our approach by applying it to two novel biological applications: quantifying the clearance of tau protein aggregates, a critical marker for neurodegenerative diseases, and differentiating between two distinct morphological features (morphotypes) of TAR DNA-binding protein 43 proteinopathy, potentially associated to different TDP-43 strains, each exhibiting unique seeding and spreading properties. We anticipate that this versatile approach will significantly enhance the way we study cellular structures across various biological contexts, elucidating their roles in disease development and progression.

Quantitative Single-Molecule Localization Microscopy

Super-resolution fluorescence microscopy allows the investigation of cellular structures at nanoscale resolution using light. Current developments in super-resolution microscopy have focused on reliable quantification of the underlying biological data. In this review, we first describe the basic principles of super-resolution microscopy techniques such as stimulated emission depletion (STED) microscopy and single-molecule localization microscopy (SMLM), and then give a broad overview of methodological developments to quantify super-resolution data, particularly those geared toward SMLM data. We cover commonly used techniques such as spatial point pattern analysis, colocalization, and protein copy number quantification but also describe more advanced techniques such as structural modeling, single-particle tracking, and biosensing. Finally, we provide an outlook on exciting new research directions to which quantitative super-resolution microscopy might be applied.

TorsinA is essential for the timing and localization of neuronal nuclear pore complex biogenesis

Nuclear pore complexes (NPCs) regulate information transfer between the nucleus and cytoplasm. NPC defects are linked to several neurological diseases, but the processes governing NPC biogenesis and spatial organization are poorly understood. Here, we identify a temporal window of strongly upregulated NPC biogenesis during neuronal maturation. We demonstrate that the AAA+ protein torsinA, whose loss of function causes the neurodevelopmental movement disorder DYT-TOR1A (DYT1) dystonia, coordinates NPC spatial organization during this period without impacting total NPC density. Using a new mouse line in which endogenous Nup107 is Halo-Tagged, we find that torsinA is essential for correct localization of NPC formation. In the absence of torsinA, the inner nuclear membrane buds excessively at sites of mislocalized, nascent NPCs, and NPC assembly completion is delayed. Our work implies that NPC spatial organization and number are independently regulated and suggests that torsinA is critical for the normal localization and assembly kinetics of NPCs.

MINSTED nanoscopy enters the Ångström localization range

Super-resolution techniques have achieved localization precisions in the nanometer regime. Here we report all-optical, room temperature localization of fluorophores with precision in the Ångström range. We built on the concept of MINSTED nanoscopy where precision is increased by encircling the fluorophore with the low-intensity central region of a stimulated emission depletion (STED) donut beam while constantly increasing the absolute donut power. By blue-shifting the STED beam and separating fluorophores by on/off switching, individual fluorophores bound to a DNA strand are localized with σ = 4.7 Å, corresponding to a fraction of the fluorophore size, with only 2,000 detected photons. MINSTED fluorescence nanoscopy with single-digit nanometer resolution is exemplified by imaging nuclear pore complexes and the distribution of nuclear lamin in mammalian cells labeled by transient DNA hybridization. Because our experiments yield a localization precision σ = 2.3 Å, estimated for 10,000 detected photons, we anticipate that MINSTED will open up new areas of application in the study of macromolecular complexes in cells.

Fluorescence-based super-resolution-microscopy strategies for chromatin studies

Super-resolution microscopy (SRM) is a prime tool to study chromatin organisation at near biomolecular resolution in the native cellular environment. With fluorescent labels DNA, chromatin-associated proteins and specific epigenetic states can be identified with high molecular specificity. The aim of this review is to introduce the field of diffraction-unlimited SRM to enable an informed selection of the most suitable SRM method for a specific chromatin-related research question. We will explain both diffraction-unlimited approaches (coordinate-targeted and stochastic-localisation-based) and list their characteristic spatio-temporal resolutions, live-cell compatibility, image-processing, and ability for multi-colour imaging. As the increase in resolution, compared to, e.g. confocal microscopy, leads to a central role of the sample quality, important considerations for sample preparation and concrete examples of labelling strategies applicable to chromatin research are discussed. To illustrate how SRM-based methods can significantly improve our understanding of chromatin functioning, and to serve as an inspiring starting point for future work, we conclude with examples of recent applications of SRM in chromatin research.

Machine learning framework to segment sarcomeric structures in SMLM data

Object detection is an image analysis task with a wide range of applications, which is difficult to accomplish with traditional programming. Recent breakthroughs in machine learning have made significant progress in this area. However, these algorithms are generally compatible with traditional pixelated images and cannot be directly applied for pointillist datasets generated by single molecule localization microscopy (SMLM) methods. Here, we have improved the averaging method developed for the analysis of SMLM images of sarcomere structures based on a machine learning object detection algorithm. The ordered structure of sarcomeres allows us to determine the location of the proteins more accurately by superimposing SMLM images of identically assembled proteins. However, the area segmentation process required for averaging can be extremely time-consuming and tedious. In this work, we have automated this process. The developed algorithm not only finds the regions of interest, but also classifies the localizations and identifies the true positive ones. For training, we used simulations to generate large amounts of labelled data. After tuning the neural network's internal parameters, it could find the localizations associated with the structures we were looking for with high accuracy. We validated our results by comparing them with previous manual evaluations. It has also been proven that the simulations can generate data of sufficient quality for training. Our method is suitable for the identification of other types of structures in SMLM data.

Single molecule imaging simulations with advanced fluorophore photophysics

Advanced fluorescence imaging techniques such as single-molecule localization microscopy (SMLM) fundamentally rely on the photophysical behavior of the employed fluorophores. This behavior is generally complex and impacts data quality in a subtle manner. A simulation software named Single-Molecule Imaging Simulator (SMIS) is introduced that simulates a widefield microscope and incorporates fluorophores with their spectral and photophysical properties. With SMIS, data collection schemes combining 3D, multicolor, single-particle-tracking or quantitative SMLM can be implemented. The influence of advanced fluorophore characteristics, imaging conditions, and environmental parameters can be evaluated, facilitating the design of real experiments and their proper interpretation.

Maximum-likelihood model fitting for quantitative analysis of SMLM data

Quantitative data analysis is important for any single-molecule localization microscopy (SMLM) workflow to extract biological insights from the coordinates of the single fluorophores. However, current approaches are restricted to simple geometries or require identical structures. Here, we present LocMoFit (Localization Model Fit), an open-source framework to fit an arbitrary model to localization coordinates. It extracts meaningful parameters from individual structures and can select the most suitable model. In addition to analyzing complex, heterogeneous and dynamic structures for in situ structural biology, we demonstrate how LocMoFit can assemble multi-protein distribution maps of six nuclear pore components, calculate single-particle averages without any assumption about geometry or symmetry, and perform a time-resolved reconstruction of the highly dynamic endocytic process from static snapshots. We provide extensive simulation and visualization routines to validate the robustness of LocMoFit and tutorials to enable any user to increase the information content they can extract from their SMLM data.

High-precision estimation of emitter positions using Bayesian grouping of localizations

Single-molecule localization microscopy super-resolution methods rely on stochastic blinking/binding events, which often occur multiple times from each emitter over the course of data acquisition. Typically, the blinking/binding events from each emitter are treated as independent events, without an attempt to assign them to a particular emitter. Here, we describe a Bayesian method of inferring the positions of the tagged molecules by exploring the possible grouping and combination of localizations from multiple blinking/binding events. The results are position estimates of the tagged molecules that have improved localization precision and facilitate nanoscale structural insights. The Bayesian framework uses the localization precisions to learn the statistical distribution of the number of blinking/binding events per emitter and infer the number and position of emitters. We demonstrate the method on a range of synthetic data with various emitter densities, DNA origami constructs and biological structures using DNA-PAINT and dSTORM data. We show that under some experimental conditions it is possible to achieve sub-nanometer precision.

splitSMLM, a spectral demixing method for high-precision multi-color localization microscopy applied to nuclear pore complexes

Single molecule localization microscopy (SMLM) with a dichroic image splitter can provide invaluable multi-color information regarding colocalization of individual molecules, but it often suffers from technical limitations. Classical demixing algorithms tend to give suboptimal results in terms of localization precision and correction of chromatic errors. Here we present an image splitter based multi-color SMLM method (splitSMLM) that offers much improved localization precision and drift correction, compensation of chromatic distortions, and optimized performance of fluorophores in a specific buffer to equalize their reactivation rates for simultaneous imaging. A novel spectral demixing algorithm, SplitViSu, fully preserves localization precision with essentially no data loss and corrects chromatic errors at the nanometer scale. Multi-color performance is further improved by using optimized fluorophore and filter combinations. Applied to three-color imaging of the nuclear pore complex (NPC), this method provides a refined positioning of the individual NPC proteins and reveals that Pom121 clusters act as NPC deposition loci, hence illustrating strength and general applicability of the method.

The development of an image splitter based multi-colour single-molecule localization microscopy method (splitSMLM) in combination with a spectral demixing algorithm improves localization accuracy as exemplified by three-colour imaging of nuclear pore complex proteins.

Real-time image resolution measurement for single molecule localization microscopy

Recent advancements in single molecule localization microscopy (SMLM) have demonstrated outstanding potential applications in high-throughput and high-content screening imaging. One major limitation to such applications is to find a way to optimize imaging throughput without scarifying image quality, especially the homogeneity in image resolution, during the imaging of hundreds of field-of-views (FOVs) in heterogeneous samples. Here we introduce a real-time image resolution measurement method for SMLM to solve this problem. This method is under the heuristic framework of overall image resolution that counts on localization precision and localization density. Rather than estimating the mean localization density after completing the entire SMLM process, this method uses the spatial Poisson process to model the random activation of molecules and thus determines the localization density in real-time. We demonstrate that the method is valid in real-time resolution measurement and is effective in guaranteeing homogeneous image resolution across multiple representative FOVs with optimized imaging throughput.

Mapping molecular complexes with super-resolution microscopy and single-particle analysis

Understanding the structure of supramolecular complexes provides insight into their functional capabilities and how they can be modulated in the context of disease. Super-resolution microscopy (SRM) excels in performing this task by resolving ultrastructural details at the nanoscale with molecular specificity. However, technical limitations, such as underlabelling, preclude its ability to provide complete structures. Single-particle analysis (SPA) overcomes this limitation by combining information from multiple images of identical structures and producing an averaged model, effectively enhancing the resolution and coverage of image reconstructions. This review highlights important studies using SRM-SPA, demonstrating how it broadens our knowledge by elucidating features of key biological structures with unprecedented detail.

Joint registration of multiple point clouds for fast particle fusion in localization microscopy

SUMMARY

We present a fast particle fusion method for particles imaged with single-molecule localization microscopy. The state-of-the-art approach based on all-to-all registration has proven to work well but its computational cost scales unfavorably with the number of particles N, namely as N2. Our method overcomes this problem and achieves a linear scaling of computational cost with N by making use of the Joint Registration of Multiple Point Clouds (JRMPC) method. Straightforward application of JRMPC fails as mostly locally optimal solutions are found. These usually contain several overlapping clusters that each consist of well-aligned particles, but that have different poses. We solve this issue by repeated runs of JRMPC for different initial conditions, followed by a classification step to identify the clusters, and a connection step to link the different clusters obtained for different initializations. In this way a single well-aligned structure is obtained containing the majority of the particles.

RESULTS

We achieve reconstructions of experimental DNA-origami datasets consisting of close to 400 particles within only 10 min on a CPU, with an image resolution of 3.2 nm. In addition, we show artifact-free reconstructions of symmetric structures without making any use of the symmetry. We also demonstrate that the method works well for poor data with a low density of labeling and for 3D data.

AVAILABILITY AND IMPLEMENTATION

The code is available for download from https://github.com/wexw/Joint-Registration-of-Multiple-Point-Clouds-for-Fast-Particle-Fusion-in-Localization-Microscopy.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deciphering a hexameric protein complex with Angstrom optical resolution

Cryogenic optical localization in three dimensions (COLD) was recently shown to resolve up to four binding sites on a single protein. However, because COLD relies on intensity fluctuations that result from the blinking behavior of fluorophores, it is limited to cases where individual emitters show different brightness. This significantly lowers the measurement yield. To extend the number of resolved sites as well as the measurement yield, we employ partial labeling and combine it with polarization encoding in order to identify single fluorophores during their stochastic blinking. We then use a particle classification scheme to identify and resolve heterogenous subsets and combine them to reconstruct the three-dimensional arrangement of large molecular complexes. We showcase this method (polarCOLD) by resolving the trimer arrangement of proliferating cell nuclear antigen (PCNA) and six different sites of the hexamer protein Caseinolytic Peptidase B (ClpB) of Thermus thermophilus in its quaternary structure, both with Angstrom resolution. The combination of polarCOLD and single-particle cryogenic electron microscopy (cryoEM) promises to provide crucial insight into intrinsic heterogeneities of biomolecular structures. Furthermore, our approach is fully compatible with fluorescent protein labeling and can, thus, be used in a wide range of studies in cell and membrane biology.

Super-Resolution Microscopy for Structural Cell Biology

Super-resolution microscopy techniques, and specifically single-molecule localization microscopy (SMLM), are approaching nanometer resolution inside cells and thus have great potential to complement structural biology techniques such as electron microscopy for structural cell biology. In this review, we introduce the different flavors of super-resolution microscopy, with a special emphasis on SMLM and MINFLUX (minimal photon flux). We summarize recent technical developments that pushed these localization-based techniques to structural scales and review the experimental conditions that are key to obtaining data of the highest quality. Furthermore, we give an overview of different analysis methods and highlight studies that used SMLM to gain structural insights into biologically relevant molecular machines. Ultimately, we give our perspective on what is needed to push the resolution of these techniques even further and to apply them to investigating dynamic structural rearrangements in living cells. Expected final online publication date for the Annual Review of Biophysics, Volume 51 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Raw Data to Results: A Hands-On Introduction and Overview of Computational Analysis for Single-Molecule Localization Microscopy

Single-molecule localization microscopy (SMLM) is an advanced microscopy method that uses the blinking of fluorescent molecules to determine the position of these molecules with a resolution below the diffraction limit (∼5-40 nm). While SMLM imaging itself is becoming more popular, the computational analysis surrounding the technique is still a specialized area and often remains a "black box" for experimental researchers. Here, we provide an introduction to the required computational analysis of SMLM imaging, post-processing and typical data analysis. Importantly, user-friendly, ready-to-use and well-documented code in Python and MATLAB with exemplary data is provided as an interactive experience for the reader, as well as a starting point for further analysis. Our code is supplemented by descriptions of the computational problems and their implementation. We discuss the state of the art in computational methods and software suites used in SMLM imaging and data analysis. Finally, we give an outlook into further computational challenges in the field.

Bench-Top Fabrication of Single-Molecule Nanoarrays by DNA Origami Placement

Large-scale nanoarrays of single biomolecules enable high-throughput assays while unmasking the underlying heterogeneity within ensemble populations. Until recently, creating such grids which combine the advantages of microarrays and single-molecule experiments (SMEs) has been particularly challenging due to the mismatch between the size of these molecules and the resolution of top-down fabrication techniques. DNA origami placement (DOP) combines two powerful techniques to address this issue: (i) DNA origami, which provides a ∼100 nm self-assembled template for single-molecule organization with 5 nm resolution and (ii) top-down lithography, which patterns these DNA nanostructures, transforming them into functional nanodevices via large-scale integration with arbitrary substrates. Presently, this technique relies on state-of-the-art infrastructure and highly trained personnel, making it prohibitively expensive for researchers. Here, we introduce a cleanroom-free, $1 benchtop technique to create meso-to-macro-scale DNA origami nanoarrays using self-assembled colloidal nanoparticles, thereby circumventing the need for top-down fabrication. We report a maximum yield of 74%, 2-fold higher than the statistical limit of 37% imposed on non-specific molecular loading alternatives. Furthermore, we provide a proof-of-principle for the ability of this nanoarray platform to transform traditionally low-throughput, stochastic, single-molecule assays into high-throughput, deterministic ones, without compromising data quality. Our approach has the potential to democratize single-molecule nanoarrays and demonstrates their utility as a tool for biophysical assays and diagnostics.

Fluorescence microscopy methods for examining telomeres during cell aging

Telomeres are protective structures, composed of nucleic acids and a complex protein mixture, located at the end of the chromosomes. They play an important role in preventing genomic instability and ensuring cell health. Defects in telomere integrity result in cell dysfunction and the development of diseases, including neurodegenerative disorders, cancer and premature aging syndromes, among others. Loss of telomere integrity during normal cell aging also initiates DNA damage signals that culminate in the senescence phenotype. Fluorescence microscopy has allowed researchers to study the dynamics, shape, localization, and co-distribution of telomeres with proteins of interest. The microscopy tools to investigate these structures have evolved, making it possible to understand in greater detail the molecular mechanisms affecting telomeres that contribute to cell aging and the development of age-related diseases. Using human fibroblasts as an example, we will highlight several characteristics of telomeres that can be investigated using three different microscopy systems, including wide-field microscopy, and the two super-resolution techniques called 3D Structured Illumination Microscopy (3D-SIM) and direct Stochastic Optical Reconstruction Microscopy (dSTORM). In this review, we will also discuss their limitations and highlight their importance in answering telomere-related scientific questions.

Three-dimensional superresolution fluorescence microscopy maps the variable molecular architecture of the nuclear pore complex

Nuclear pore complexes (NPCs) are large macromolecular machines that mediate the traffic between the nucleus and the cytoplasm. In vertebrates, each NPC consists of ∼1000 proteins, termed nucleoporins, and has a mass of more than 100 MDa. While a pseudo-atomic static model of the central scaffold of the NPC has recently been assembled by integrating data from isolated proteins and complexes, many structural components still remain elusive due to the enormous size and flexibility of the NPC. Here, we explored the power of three-dimensional (3D) superresolution microscopy combined with computational classification and averaging to explore the 3D structure of the NPC in single human cells. We show that this approach can build the first integrated 3D structural map containing both central as well as peripheral NPC subunits with molecular specificity and nanoscale resolution. Our unbiased classification of more than 10,000 individual NPCs indicates that the nuclear ring and the nuclear basket can adopt different conformations. Our approach opens up the exciting possibility to relate different structural states of the NPC to function in situ.

Detecting structural heterogeneity in single-molecule localization microscopy data

Particle fusion for single molecule localization microscopy improves signal-to-noise ratio and overcomes underlabeling, but ignores structural heterogeneity or conformational variability. We present a-priori knowledge-free unsupervised classification of structurally different particles employing the Bhattacharya cost function as dissimilarity metric. We achieve 96% classification accuracy on mixtures of up to four different DNA-origami structures, detect rare classes of origami occuring at 2% rate, and capture variation in ellipticity of nuclear pore complexes.

3D particle averaging and detection of macromolecular symmetry in localization microscopy

Single molecule localization microscopy offers in principle resolution down to the molecular level, but in practice this is limited primarily by incomplete fluorescent labeling of the structure. This missing information can be completed by merging information from many structurally identical particles. In this work, we present an approach for 3D single particle analysis in localization microscopy which hugely increases signal-to-noise ratio and resolution and enables determining the symmetry groups of macromolecular complexes. Our method does not require a structural template, and handles anisotropic localization uncertainties. We demonstrate 3D reconstructions of DNA-origami tetrahedrons, Nup96 and Nup107 subcomplexes of the nuclear pore complex acquired using multiple single molecule localization microscopy techniques, with their structural symmetry deducted from the data.

Nanoscale Pattern Extraction from Relative Positions of Sparse 3D Localizations

Inferring the organization of fluorescently labeled nanosized structures from single molecule localization microscopy (SMLM) data, typically obscured by stochastic noise and background, remains challenging. To overcome this, we developed a method to extract high-resolution ordered features from SMLM data that requires only a low fraction of targets to be localized with high precision. First, experimentally measured localizations are analyzed to produce relative position distributions (RPDs). Next, model RPDs are constructed using hypotheses of how the molecule is organized. Finally, a statistical comparison is used to select the most likely model. This approach allows pattern recognition at sub-1% detection efficiencies for target molecules, in large and heterogeneous samples and in 2D and 3D data sets. As a proof-of-concept, we infer ultrastructure of Nup107 within the nuclear pore, DNA origami structures, and α-actinin-2 within the cardiomyocyte Z-disc and assess the quality of images of centrioles to improve the averaged single-particle reconstruction.

A workflow for sizing oligomeric biomolecules based on cryo single molecule localization microscopy

Single molecule localization microscopy (SMLM) has enormous potential for resolving subcellular structures below the diffraction limit of light microscopy: Localization precision in the low digit nanometer regime has been shown to be achievable. In order to record localization microscopy data, however, sample fixation is inevitable to prevent molecular motion during the rather long recording times of minutes up to hours. Eventually, it turns out that preservation of the sample's ultrastructure during fixation becomes the limiting factor. We propose here a workflow for data analysis, which is based on SMLM performed at cryogenic temperatures. Since molecular dipoles of the fluorophores are fixed at low temperatures, such an approach offers the possibility to use the orientation of the dipole as an additional information for image analysis. In particular, assignment of localizations to individual dye molecules becomes possible with high reliability. We quantitatively characterized the new approach based on the analysis of simulated oligomeric structures. Side lengths can be determined with a relative error of less than 1% for tetramers with a nominal side length of 5 nm, even if the assumed localization precision for single molecules is more than 2 nm.

Quantitative Data Analysis in Single-Molecule Localization Microscopy

Super-resolution microscopy, and specifically single-molecule localization microscopy (SMLM), is becoming a transformative technology for cell biology, as it allows the study of cellular structures with nanometer resolution. Here, we review a wide range of data analyses approaches for SMLM that extract quantitative information about the distribution, size, shape, spatial organization, and stoichiometry of macromolecular complexes to guide biological interpretation. We present a case study using the nuclear pore complex as an example that highlights the power of combining complementary approaches by identifying its symmetry, ringlike structure, and protein copy number. In face of recent technical and computational advances, this review serves as a guideline for selecting appropriate analysis tools and controls to exploit the potential of SMLM for a wide range of biological questions.

25th Anniversary of STED Microscopy and the 20th Anniversary of SIM: feature introduction

This feature issue commemorating 25 years of STED microscopy and 20 years of SIM is intended to highlight the incredible progress and growth in the field of superresolution microscopy since Stefan Hell and Jan Wichmann published the article Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy in Optics Letters in 1994.

Lessons Learned in a Decade of Research Software Engineering GPU Applications

After years of using Graphics Processing Units (GPUs) to accelerate scientific applications in fields as varied as tomography, computer vision, climate modeling, digital forensics, geospatial databases, particle physics, radio astronomy, and localization microscopy, we noticed a number of technical, socio-technical, and non-technical challenges that Research Software Engineers (RSEs) may run into. While some of these challenges, such as managing different programming languages within a project, or having to deal with different memory spaces, are common to all software projects involving GPUs, others are more typical of scientific software projects. Among these challenges we include changing resolutions or scales, maintaining an application over time and making it sustainable, and evaluating both the obtained results and the achieved performance.

Localization microscopy at doubled precision with patterned illumination

MINFLUX offers a breakthrough in single molecule localization precision, but is limited in field-of-view. Here, we combine centroid estimation and illumination pattern induced photon count variations in a conventional widefield imaging setup to extract position information over a typical micron sized field-of-view. We show a near twofold improvement in precision over standard localization with the same photon count on DNA-origami nano-structures and tubulin in cells, using DNA-PAINT and STORM imaging.

Single-molecule localization microscopy as nonlinear inverse problem

We present a statistical framework to model the spatial distribution of molecules based on a single-molecule localization microscopy (SMLM) dataset. The latter consists of a collection of spatial coordinates and their associated uncertainties. We describe iterative parameter-estimation algorithms based on this framework, as well as a sampling algorithm to numerically evaluate the complete posterior distribution. We demonstrate that the inverse computation can be viewed as a type of image restoration process similar to the classical image deconvolution methods, except that it is performed on SMLM images. We further discuss an application of our statistical framework in the task of particle fusion using SMLM data. We show that the fusion algorithm based on our model outperforms the current state-of-the-art in terms of both accuracy and computational cost.

About samples, giving examples: Optimized Single Molecule Localization Microscopy

Super-resolution microscopy has profoundly transformed how we study the architecture of cells, revealing unknown structures and refining our view of cellular assemblies. Among the various techniques, the resolution of Single Molecule Localization Microscopy (SMLM) can reach the size of macromolecular complexes and offer key insights on their nanoscale arrangement in situ. SMLM is thus a demanding technique and taking advantage of its full potential requires specifically optimized procedures. Here we describe how we perform the successive steps of an SMLM workflow, focusing on single-color Stochastic Optical Reconstruction Microscopy (STORM) as well as multicolor DNA Points Accumulation for imaging in Nanoscale Topography (DNA-PAINT) of fixed samples. We provide detailed procedures for careful sample fixation and immunostaining of typical cellular structures: cytoskeleton, clathrin-coated pits, and organelles. We then offer guidelines for optimal imaging and processing of SMLM data in order to optimize reconstruction quality and avoid the generation of artifacts. We hope that the tips and tricks we discovered over the years and detail here will be useful for researchers looking to make the best possible SMLM images, a pre-requisite for meaningful biological discovery.

Deformed alignment of super-resolution images for semi-flexible structures

Due to low labeling efficiency and structural heterogeneity in fluorescence-based single-molecule localization microscopy (SMLM), image alignment and quantitative analysis is often required to make accurate conclusions on the spatial relationships between proteins. Cryo-electron microscopy (EM) image alignment procedures have been applied to average structures taken with super-resolution microscopy. However, unlike cryo-EM, the much larger cellular structures analyzed by super-resolution microscopy are often heterogeneous, resulting in misalignment. And the light-microscopy image library is much smaller, which makes classification challenging. To overcome these two challenges, we developed a method to deform semi-flexible ring-shaped structures and then align the 3D structures without classification. These algorithms can register semi-flexible structures with an accuracy of several nanometers in short computation time and with greatly reduced memory requirements. We demonstrated our methods by aligning experimental Stochastic Optical Reconstruction Microscopy (STORM) images of ciliary distal appendages and simulated structures. Symmetries, dimensions, and locations of protein complexes in 3D are revealed by the alignment and averaging for heterogeneous, tilted, and under-labeled structures.